A new bulk adaptive habit microphysical model including deposition coefficient prediction and its application in different model frameworks

Open Access
- Author:
- Sokolowsky, George Alexander
- Graduate Program:
- Meteorology
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- March 15, 2018
- Committee Members:
- Jerry Y Harrington, Thesis Advisor/Co-Advisor
Eugene Edmund Clothiaux, Committee Member
Matthew Robert Kumjian, Committee Member - Keywords:
- cloud physics
atmospheric ice modeling
deposition coefficient
box model
parcel model
bulk particle size distribution
bulk model
faceted ice crystals
cirrus
ice microphysics
adaptive habit
deposition coefficient prediction
vapor deposition ice growth - Abstract:
- We have developed a new bulk microphysical model for vapor deposition growth of atmospheric pristine ice crystals. This model builds upon the single-particle adaptive habit growth model (Harrington et al., 2013), utilizes a bulk particle size distribution (PSD), and incorporates the deposition coefficient prediction method from the Kinetically Limited Adaptive Habit (KLAH) model (Zhang and Harrington, 2014). Bulk PSD growth is predicted using the first and third moments of a gamma particle size distribution and deposition coefficient prediction uses a ratio of the third and second moments of the characteristic axis lengths as an input length scale. The new bulk model’s accuracy has been assessed through extensive comparison to a Lagrangian bin version of the KLAH bin model. Model frameworks that the bulk parameterization has been used in include a fixed “box model” and a variable 1-dimensional Lagrangian parcel model. Tests compared the water vapor and ice water mixing ratios, ice number concentrations, axis lengths, aspect ratios, and deposition coefficients predicted by the bulk model as compared to the bin solutions. Relative errors for key growth variables are generally within 30%. Considering the complexity of modeling nonspherical crystal growth, these results compare well to the accuracy of predicting mass growth of a bulk PSD of spherical crystals, which can present as much as a 20% relative error between bin and bulk PSDs. This parameterization is intended to be implemented into the Weather Research and Forecasting Model (WRF) as a part of the particle property variable (PPV) model of Jensen et al. (2017).